app.py

import xgboost as xgb
import gradio as gr
import markdown

# Load the saved models
loaded_model_child = xgb.XGBRegressor()
loaded_model_adult = xgb.XGBRegressor()
loaded_model_birth = xgb.XGBRegressor()
loaded_model_sixty = xgb.XGBRegressor()
loaded_model_child.load_model("xgb_child_mort.model")
loaded_model_adult.load_model("xgb_adult_mort.model")
loaded_model_birth.load_model("xgb_life_expect_birth.model")
loaded_model_sixty.load_model("xgb_life_expect_sixty.model")

# Read the markdown file for description
with open("mraw_description.md", "r") as file:
    description_markdown = file.read()

# Read the markdown file for article
with open("mraw_article.md", "r") as file:
    article_markdown = file.read()

# Define the prediction function
def predict_mortality_rates(percentage_clean_water):
    # Make predictions using all 4 models
    prediction_child = loaded_model_child.predict([[percentage_clean_water]])
    prediction_adult = loaded_model_adult.predict([[percentage_clean_water]])
    prediction_birth = loaded_model_birth.predict([[percentage_clean_water]])
    prediction_sixty = loaded_model_sixty.predict([[percentage_clean_water]])
    
    return (
        f"{prediction_child[0]:.2f}",
        f"{prediction_adult[0]:.2f}",
        f"{prediction_birth[0]:.2f}",
        f"{prediction_sixty[0]:.2f}"
    )

# Convert markdown content to HTML for description
description_html = markdown.markdown(description_markdown)

# Convert markdown content to HTML for article
article_html = markdown.markdown(article_markdown)

# Create the Gradio interface
input_text = gr.Number(label="Enter the Percentage of People using Clean Drinking Water in a Country")
output_text1 = gr.Textbox(label="Predicted Children (under 5) Mortality rate")
output_text2 = gr.Textbox(label="Predicted Adult (15 and above) Mortality rate")
output_text3 = gr.Textbox(label="Predicted Life Expectancy at Birth (in years)")
output_text4 = gr.Textbox(label="Predicted Life Expectancy at age 60 (in years)")

interface = gr.Interface(
    title="Mortality Rates prediction based on Access to Water",
    description=description_html,
    fn=predict_mortality_rates,
    inputs=input_text,
    outputs=[output_text1, output_text2, output_text3, output_text4],
    theme=gr.themes.Base(),
    article=article_html
)

# Launch the Gradio interface
interface.launch()